Publications by authors named "V A Gombolevskiy"

13 Publications

[Adrenal imaging: anatomy and pathology (literature review)].

Probl Endokrinol (Mosk) 2021 Jun 7;67(3):26-36. Epub 2021 Jun 7.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department.

This literature review focuses on the normal adrenal gland anatomy and typical imaging features necessary to evaluate benign and malignant lesions. In particular, adenoma, pheochromocytoma, metastases and adrenocortical carcinoma were discussed as some of the most common lesions. For this purpose, a review of relevant local and international literature sources up to January 2021 was conducted.In many cases, adrenal incidentalomas have distinctive features allowing characterization using noninvasive methods. It is possible to suspect a malignant nature and promptly refer the patient for the necessary invasive examinations in some cases. -Computed tomography, especially with intravenous contrast enhancement, is the primary imaging modality because it enables differential diagnosis. Magnetic resonance tomography remains a sensitive method in lesion detection and follow-up but is not very specific for determining the malignant potential. Positron emission computed tomography also remains an additional method and is used mainly for differential diagnosis of malignant tumors, detecting metastases and recurrences after surgical treatment. Ultrasound has a limited role but is nevertheless of great importance in the pediatric population, especially newborns. Promising techniques such as radiomics and dual-energy CT can expand imaging capabilities and improve diagnostic accuracy.Because adrenal lesions are often incidentally detected by imaging performed for other reasons, it is vital to interpret such findings correctly. This review should give the reader a broad overview of how different imaging modalities can evaluate adrenal pathology and guide radiologists and clinicians.
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http://dx.doi.org/10.14341/probl12752DOI Listing
June 2021

Braincase anatomy of extant Crocodylia, with new insights into the development and evolution of the neurocranium in crocodylomorphs.

J Anat 2021 Jun 27. Epub 2021 Jun 27.

Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.

Present-day crocodylians exhibit a remarkably akinetic skull with a highly modified braincase. We present a comprehensive description of the neurocranial osteology of extant crocodylians, with notes on the development of individual skeletal elements and a discussion of the terminology used for this project. The quadrate is rigidly fixed by multiple contacts with most braincase elements. The parabasisphenoid is sutured to the pterygoids (palate) and the quadrate (suspensorium); as a result, the basipterygoid joint is completely immobilized. The prootic is reduced and externally concealed by the quadrate. It has a verticalized buttress that participates in the canal for the temporal vasculature. The ventrolateral processes of the otoccipitals completely cover the posteroventral region of the braincase, enclose the occipital nerves and blood vessels in narrow bony canals and also provide additional sutural contacts between the braincase elements and further consolidate the posterior portion of the crocodylian skull. The otic capsule of crocodylians has a characteristic cochlear prominence that corresponds to the lateral route of the perilymphatic sac. Complex internal structures of the otoccipital (extracapsular buttress) additionally arrange the neurovascular structures of the periotic space of the cranium. Most of the braincase elements of crocodylians are excavated by the paratympanic pneumatic sinuses. The braincase in various extant crocodylians has an overall similar structure with some consistent variation between taxa. Several newly observed features of the braincase are present in Gavialis gangeticus and extant members of Crocodylidae to the exclusion of alligatorids: the reduced exposure of the prootic buttress on the floor of the temporal canal, the sagittal nuchal crest of the supraoccipital projecting posteriorly beyond the postoccipital processes and the reduced paratympanic pneumaticity. The most distinctive features of the crocodylian braincase (fixed quadrate and basipterygoid joint, consolidated occiput) evolved relatively rapidly at the base of Crocodylomorpha and accompanied the initial diversification of this clade during the Late Triassic and Early Jurassic. We hypothesize that profound rearrangements in the individual development of the braincases of basal crocodylomorphs underlie these rapid evolutionary modifications. These rearrangements are likely reflected in the embryonic development of extant crocodylians and include the involvement of neomorphic dermal anlagen in different portions of the developing chondrocranium, the extensive ossification of the palatoquadrate cartilage as a single expanded quadrate and the anteromedial inclination of the quadrate.
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http://dx.doi.org/10.1111/joa.13490DOI Listing
June 2021

A phantom study to optimise the automatic tube current modulation for chest CT in COVID-19.

Eur Radiol Exp 2021 05 28;5(1):21. Epub 2021 May 28.

Radiation Protection of Patients Unit, International Atomic Energy Agency, Vienna, Austria.

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic. The expert organisations recommend more cautious use of thoracic computed tomography (CT), opting for low-dose protocols. We aimed at determining a threshold value of automatic tube current modulation noise index below which there is a chance to miss an onset of ground-glass opacities (GGO) in COVID-19. A team of radiologists and medical physicists performed 25 phantom CT studies using different automatic tube current modulation settings (Exposure3D technology). We then conducted a retrospective evaluation of the chest CT images from 22 patients with COVID-19 and calculated the density difference between the GGO and unaffected tissue. Finally, the results were matched to the phantom study results to determine the minimum noise index threshold value. The minimum density difference at the onset of COVID-19 was 252 HU (p < 0.001). This was found to correspond to the Exposure 3D noise index of 36. We established the noise index threshold of 36 for the Canon scanner without iterative reconstructions, allowing for a decrease in the dose-length product by 80%. The proposed protocol needs to be validated in a prospective study.
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http://dx.doi.org/10.1186/s41747-021-00218-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159722PMC
May 2021

A simplified cluster model and a tool adapted for collaborative labeling of lung cancer CT scans.

Comput Methods Programs Biomed 2021 Jul 18;206:106111. Epub 2021 Apr 18.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Petrovka str., 24, Moscow, 127051, Russia; Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Vavilova str., 44/2, Moscow, 119333, Russia. Electronic address:

Background And Objective: Lung cancer is the most common type of cancer with a high mortality rate. Early detection using medical imaging is critically important for the long-term survival of the patients. Computer-aided diagnosis (CAD) tools can potentially reduce the number of incorrect interpretations of medical image data by radiologists. Datasets with adequate sample size, annotation, and truth are the dominant factors in developing and training effective CAD algorithms. The objective of this study was to produce a practical approach and a tool for the creation of medical image datasets.

Methods: The proposed model uses the modified maximum transverse diameter approach to mark a putative lung nodule. The modification involves the possibility to use a set of overlapping spheres of appropriate size to approximate the shape of the nodule. The algorithm embedded in the model also groups the marks made by different readers for the same lesion. We used the data of 536 randomly selected patients of Moscow outpatient clinics to create a dataset of standard-dose chest computed tomography (CT) scans utilizing the double-reading approach with arbitration. Six volunteer radiologists independently produced a report for each scan using the proposed model with the main focus on the detection of lesions with sizes ranging from 3 to 30 mm. After this, an arbitrator reviewed their marks and annotations.

Results: The maximum transverse diameter approach outperformed the alternative methods (3D box, ellipsoid, and complete outline construction) in a study of 10,000 computer-generated tumor models of different shapes in terms of accuracy and speed of nodule shape approximation. The markup and annotation of the CTLungCa-500 dataset revealed 72 studies containing no lung nodules. The remaining 464 CT scans contained 3151 lesions marked by at least one radiologist: 56%, 14%, and 29% of the lesions were malignant, benign, and non-nodular, respectively. 2887 lesions have the target size of 3-30 mm. Only 70 nodules were uniformly identified by all the six readers. An increase in the number of independent readers providing CT scans interpretations led to an accuracy increase associated with a decrease in agreement. The dataset markup process took three working weeks.

Conclusions: The developed cluster model simplifies the collaborative and crowdsourced creation of image repositories and makes it time-efficient. Our proof-of-concept dataset provides a valuable source of annotated medical imaging data for training CAD algorithms aimed at early detection of lung nodules. The tool and the dataset are publicly available at https://github.com/Center-of-Diagnostics-and-Telemedicine/FAnTom.git and https://mosmed.ai/en/datasets/ct_lungcancer_500/, respectively.
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http://dx.doi.org/10.1016/j.cmpb.2021.106111DOI Listing
July 2021

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

Med Image Anal 2021 07 1;71:102054. Epub 2021 Apr 1.

Skolkovo Institute of Science and Technology, Moscow, Russia. Electronic address:

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.
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http://dx.doi.org/10.1016/j.media.2021.102054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015379PMC
July 2021
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